Genetic architecture of complex traits: Large phenotypic effects and pervasive epistasis

Shao, Haifeng; Burrage, Lindsay C.; Sinasac, David S.; Hill, Annie E.; Ernest, Sheila; O’Brien, William E.; Courtland, Hayden-William; Jepsen, Karl J.; Kirby, Andrew; Kulbokas, Edward J.; Daly, Mark J.; Broman, Karl W.; Lander, Eric S.; Nadeau, Joseph H.

doi:10.1073/pnas.0810388105

Cited by 249 publications

(266 citation statements)

References 32 publications

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“…However, we propose that these additional loci, in combination, can contribute to the high mtDNA instability observed in the extreme F 1 segregant (Figure 2, arrowhead). Similar epistatic interactions that are hard to detect by conventional genetic crosses have been uncovered by using whole-chromosome substitutions in mammals (Shao et al 2008).…”

Section: Discussionmentioning

confidence: 99%

Polymorphisms in Multiple Genes Contribute to the Spontaneous Mitochondrial Genome Instability ofSaccharomyces cerevisiaeS288C Strains

et al. 2009

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The mitochondrial genome (mtDNA) is required for normal cellular function; inherited and somatic mutations in mtDNA lead to a variety of diseases. Saccharomyces cerevisiae has served as a model to study mtDNA integrity, in part because it can survive without mtDNA. A measure of defective mtDNA in S. cerevisiae is the formation of petite colonies. The frequency at which spontaneous petite colonies arise varies by $100-fold between laboratory and natural isolate strains. To determine the genetic basis of this difference, we applied quantitative trait locus (QTL) mapping to two strains at the opposite extremes of the phenotypic spectrum: the widely studied laboratory strain S288C and the vineyard isolate RM11-1a. Four main genetic determinants explained the phenotypic difference. Alleles of SAL1, CAT5, and MIP1 contributed to the high petite frequency of S288C and its derivatives by increasing the formation of petite colonies. By contrast, the S288C allele of MKT1 reduced the formation of petite colonies and compromised the growth of petite cells. The former three alleles were found in the EM93 strain, the founder that contributed $88% of the S288C genome. Nearly all of the phenotypic difference between S288C and RM11-1a was reconstituted by introducing the common alleles of these four genes into the S288C background. In addition to the nuclear gene contribution, the source of the mtDNA influenced its stability. These results demonstrate that a few rare genetic variants with individually small effects can have a profound phenotypic effect in combination. Moreover, the polymorphisms identified in this study open new lines of investigation into mtDNA maintenance.

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Section: Discussionmentioning

confidence: 99%

Polymorphisms in Multiple Genes Contribute to the Spontaneous Mitochondrial Genome Instability ofSaccharomyces cerevisiaeS288C Strains

et al. 2009

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“…There is no significant variation among the categories (F = 1.8, df = 5, P = 0.109). Mapping multiple traits in chromosome substitution strains in mice gives a similar picture of the genetic architecture of quantitative phenotypes (Shao et al 2008). CSS studies of 90 traits in a mouse panel and 54 traits in a rat panel identified multiple QTLs for each phenotype (Shao et al 2008).…”

Section: How Much Does Genetic Architecture Vary Between Phenotypes?mentioning

confidence: 95%

“…Mapping multiple traits in chromosome substitution strains in mice gives a similar picture of the genetic architecture of quantitative phenotypes (Shao et al 2008). CSS studies of 90 traits in a mouse panel and 54 traits in a rat panel identified multiple QTLs for each phenotype (Shao et al 2008). The average Cold Spring Harbor Laboratory Press on May 11, 2018 -Published by genome.cshlp.org Downloaded from phenotypic effects for a CSS were similar across all phenotypes in both mouse and rat panels.…”

Section: How Much Does Genetic Architecture Vary Between Phenotypes?mentioning

confidence: 95%

“…Genetic differences between a panel member and the host arise from differences on the one chromosome they do not share, thereby allowing a relatively quick and simple test or the location of a QTL. Isolating each chromosome also permits a sensitive test of epistasis, and mapping of 90 traits revealed almost half to have significant deviation from an additive model, strong evidence for epistasis (Shao et al 2008).…”

Section: Genetic Architecture: Context-dependent Effectsmentioning

confidence: 99%

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Genetic architecture of quantitative traits in mice, flies, and humans

Flint¹,

Mackay²

2009

Genome Res.

407

364

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We compare and contrast the genetic architecture of quantitative phenotypes in two genetically well-characterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, with that found in our own species from recent successes in genome-wide association studies. We show that the current model of large numbers of loci, each of small effect, is true for all species examined, and that discrepancies can be largely explained by differences in the experimental designs used. We argue that the distribution of effect size of common variants is the same for all phenotypes regardless of species, and we discuss the importance of epistasis, pleiotropy, and gene by environment interactions. Despite substantial advances in mapping quantitative trait loci, the identification of the quantitative trait genes and ultimately the sequence variants has proved more difficult, so that our information on the molecular basis of quantitative variation remains limited. Nevertheless, available data indicate that many variants lie outside genes, presumably in regulatory regions of the genome, where they act by altering gene expression. As yet there are very few instances where homologous quantitative trait loci, or quantitative trait genes, have been identified in multiple species, but the availability of high-resolution mapping data will soon make it possible to test the degree of overlap between species.Recent successes in mapping quantitative trait loci (QTLs) that contribute to phenotypic variation in humans and model organisms make it possible to address important questions about the genetic architecture of quantitative phenotypes, such as the likely number of loci, their mode of genetic action, and interaction with each other and with the environment. An understanding of the genetic architecture of quantitative traits is not only important in its own right, it will also inform studies that seek to proceed to the identification of the quantitative trait genes (QTGs) and the quantitative trait nucleotide (QTN) variants, the functional changes in DNA sequence that contribute to trait variation.In this work we use examples from two genetically wellcharacterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, to discuss how an understanding of the genetic architecture of quantitative phenotypes in model organisms informs mapping experiments in human genetics. In our discussion of the latter, we draw upon the recent successful application of genome-wide association studies (GWAS) to both quantitative phenotypes (such as height and weight) and disease phenotypes (assuming that the genetic architecture of common disease is similar, if not identical, to that of quantitative phenotypes).Genetic architecture has been the subject of a number of recent reviews, most of which deal with information from a single model organism (Mackay 2004) or review a small number of phenotypes (Kendler and Greenspan 2006). Here, our purpose is different. We tackle three relate...

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“…Moreover, the dissection of complex traits in animal models using chromosome substitution strains has shown that the number of strong effects may be larger than previously assumed and that substantial epistatic interactions account for the subadditivity of these effects. 23 Nonetheless, the detection by association analysis of more than a few effects contributing to a phenotype will demand very large samples. To limit the sample size, several strategies have been proposed.…”

Section: Discussionmentioning

confidence: 99%

A remark on rare variants

Oexle

2010

J Hum Genet

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The genetic architecture of a disease determines the epidemiological methods for its examination. Recently, Bodmer and Bonilla suggested that moderately strong, moderately rare variants contribute substantially to the genetic population attributable risk (PAR) of common diseases. In the first part of this communication, I provide a concise reconstruction of their deliberation. Variants contributing to human disease can be identified by linkage or by association tests. Risch and Merikangas analyzed the power of these tests by comparing the affected sib-pair linkage test (ASP) and the transmission disequilibrium association test (TDT). In the second part of this paper, I give an accessible reconstruction of this comparison and derive simple approximations in the low allele frequency range, directly showing that the linkage test is much more sensitive to a decrease of frequency or effect size. In the third part, I analyze a disease model whose genetic architecture is proportional to Kimura's infinite sites model. The relation between a variant's selection coefficient and its effect size in disease generation is assumed to be simple, and the number of contributing genetic variants is determined by the sum of their approximative PAR contributions. An association test (TDT) is finally applied to this disease model. For different ranges of effect size and allele frequency, I derive the minimal sample size necessary to detect at least one contributing variant. It turns out that, although the majority of contributing variants is not accessible with realistic sample sizes, a minimum of sample size may be given for moderately strong variants in the 1% frequency range.

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Genetic architecture of complex traits: Large phenotypic effects and pervasive epistasis

Cited by 249 publications

References 32 publications

Polymorphisms in Multiple Genes Contribute to the Spontaneous Mitochondrial Genome Instability ofSaccharomyces cerevisiaeS288C Strains

Polymorphisms in Multiple Genes Contribute to the Spontaneous Mitochondrial Genome Instability ofSaccharomyces cerevisiaeS288C Strains

Genetic architecture of quantitative traits in mice, flies, and humans

A remark on rare variants

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